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ObjectiveThe study aimed to evaluate the non-cancer-specific death risk and identify the risk factors affecting the non-cancer-specific survival (NCSS) in patients with primary central nervous system lymphoma (PCNSL).MethodsThis multi-center cohort study included 2497 patients with PCNSL in the Surveillance, Epidemiology and End Results (SEER) database from 2007 to 2016, with a mean follow-up of 4.54 years. The non-cancer-specific death risk in patients with PCNSL and primary central nervous system diffuse large B-cell lymphoma (PCNS-DLBCL) was evaluated using the proportion of deaths, standardized mortality ratio (SMR), and absolute excess risk (AER). Univariate and multivariate competing risk regression models were utilized to identify the risk factors of NCSS.ResultsPCNSL was the most frequent cause of death in PCNSL patients (75.03%). Non-cancer-specific causes constituted a non-negligible portion of death (20.61%). Compared with the general population, PCNSL patients had higher risks of death from cardiovascular disease (CVD) (SMR, 2.55; AER, 77.29), Alzheimer’s disease (SMR, 2.71; AER, 8.79), respiratory disease (SMR, 2.12; AER, 15.63), and other non-cancer-specific diseases (SMR, 4.12; AER, 83.12). Male sex, Black race, earlier year of diagnosis (2007–2011), being unmarried, and a lack of chemotherapy were risk factors for NCSS in patients with PCNSL and PCNS-DLBCL (all P < 0.05).ConclusionNon-cancer-specific causes were important competing causes of death in PCNSL patients. More attention is recommended to non-cancer-specific causes of death in the management of PCNSL patients.

PurposeTo study the cardiovascular death (CVD) risk in primary central nervous system lymphoma (PCNSL) patients with chemotherapy.MethodsWe obtained 2,020 PCNSL participants and 88,613 non-central nervous system lymphoma (NCNSL) participants with chemotherapy from Surveillance, Epidemiology, and End Results (SEER) database from 2004 to 2015. A 1:3 propensity score matching (PSM) was used to reduce the imbalance between PCNSL participants with and without chemotherapy, as well as the imbalance between PCNSL and NCNSL participants with chemotherapy. Competing risks regressions were conducted to evaluate the independent influence of chemotherapy on CVD.ResultsAfter 1:3 PSM, the CVD risk in PCNSL patients with chemotherapy was lower than those without chemotherapy [decreased 53%, adjusted HR, 0.469 (95% CI, 0.255–0.862; P = 0.015)] as well as NCNSL patients with chemotherapy [decreased 36%, adjusted HR in model 1, 0.636 (95% CI, 0.439–0.923; P = 0.017)]. The CVD risk of chemotherapy decreased in PCNSL patients with age at diagnosis >60 years old [adjusted HR, 0.390 (95% CI, 0.200–0.760; P = 0.006)], and those patients diagnosed at 2010 to 2015 [adjusted HR, 0.339 (95% CI, 0.118–0.970; P = 0.044)].ConclusionPCNSL patients with chemotherapy are associated with lower CVD risk. Our findings may provide new foundations for that chemotherapy is the first-line treatment for PCNSL patients, according to a cardiovascular risk perspective.

Background: Medulloblastoma (MB) is one of the most malignant neuroepithelial tumors in the central nervous system. This study aimed to establish an effective prognostic nomogram and risk grouping system for predicting overall survival (OS) of patients with MB.Materials and Methods: The nomogram was constructed based on data from the database of Surveillance, Epidemiology, and End Results (SEER). This database consisted of 2,824 patients with medulloblastoma and was used as the training cohort. The data of another additional 161 patients treated at the Sun Yat-sen University Cancer Center (SYSUCC) were used as the external validation cohort. Cox regression analysis was used to select independent prognostic factors. Concordance index (C-index) and calibration curve were used to predict the prognostic effect of the nomogram for overall survival.Results: In the training cohort, Cox regression analyses showed that the prognostic factors included histopathology, surgery, radiotherapy, chemotherapy, tumor size, dissemination, and age at diagnosis. The internal and external validated C-indexes were 0.681 and 0.644, respectively. Calibration curves showed that the nomogram was able to predict 1-, 3-, and 5-year OS for patients with MB precisely. Using the training cohort, a risk grouping system was built, which could perfectly classify patients into four risk nomogroups with a 5-year survival rate of 83.9%, 76.5%, 64.5%, and 46.8%, respectively.Conclusion: We built and validated a nomogram and risk grouping system that can provide individual prediction of OS and distinguish MB patients from different risk groups. This nomogram and risk grouping system could help clinicians making better treatment plan and prognostic assessment.